Automatic selection of cranial remodeling device configuration

ABSTRACT

A method and system for producing cranial remodeling devices to correct for head deformities in infants is described. The system operates on three dimensional digital captured data of a head to provide cranial remodeling device information for a cranial remodeling device.

RELATED APPLICATIONS

[0001] This application is a continuation-in-part of copending U.S.application Ser. No. 10/385,307 filed Mar. 10, 2003, forThree-Dimensional Image Capture System by T. Littlefield and J. Pomattoand assigned to a common assignee. The following related patentapplications are being filed on even date herewith and are assigned to acommon assignee: Ser. No. ______, Method And Apparatus For ProducingThree Dimensional Shapes by T. Littlefield, J. Pomatto and G. Kechter;Ser. No. ______, Cranial Remodeling Device Database by T. Littlefieldand J. Pomatto; Ser. No. ______, Automatic Selection of CranialRemodeling Device Trim Lines by T. Littlefield and J. Pomatto; Ser. No.______, Cranial Remodeling Device Manufacturing System by T.Littlefield, and J. Pomatto. The disclosures of the above-identifiedapplications are incorporated herein.

FIELD OF THE INVENTION

[0002] This invention pertains to a method and apparatus forautomatically providing a cranial remodeling device configuration, ingeneral, and a method and apparatus for automatically selecting thetype, style and configuration of a cranial remodeling device, inparticular.

BACKGROUND OF THE INVENTION

[0003] Cranial remodeling is utilized to correct for deformities in thehead shapes of infants. Prior to the development of the Dynamic OrthoticCranioplasty^(SM) method of cranial remodeling by Cranial Technologies,Inc, the assignee of the present invention, the only viable approach forcorrection of cranial deformities was surgical correction of the shapeof the cranium. Dynamic Orthotic Cranioplasty^(SM) utilizes a treatmentprogram in which a cranial remodeling band is custom produced for eachinfant to be treated. The band has an internal shape that produces thedesired shape of the infant's cranium.

[0004] In the past, a cranial remodeling device or band was produced byfirst obtaining a full size and accurate model of the infants actualhead shape. This first model or shape was then modified to produce asecond or desired head shape. The second or desired head shape is usedto form the cranial remodeling band for the infant. The first shape wasoriginally produced as a cast of the infant's head. The second shape wassimilarly produced as a cast of the head and manually modified to formthe desired shape.

[0005] Various arrangements have been considered in the past to automatethe process of producing cranial remodeling devices. In some of theprior arrangements a scanner is utilized to obtain three dimensionaldata of an infant's head. Such arrangements have the disadvantage inthat scanners cannot obtain instantaneous capture of data of theentirety of an infant's head. In addition, it has been proposed toutilize expert systems to operate on scanned data to produce an image ofa modified head shape from which a cranial remodeling device may befabricated. However, because each head shape is unique, even the use ofan expert system may not present an optimized solution to developingmodified shapes suitable for producing a cranial remodeling device.

[0006] Various systems are known for the capturing of images of objectsincluding live objects. One category of such systems typically utilizesa scanning technology with lasers or other beam emitting sources. Thedifficulty with systems of this type is that to scan a three-dimensionalobject, the scan times limit use of the systems to stationary objects.

[0007] A second category of image captures systems utilizes triangulatedcameras with or without projection of structured light patterns on theobject. However, these systems typically are arranged to capture athree-dimensional image of only a portion of the object. Typically suchsystems also are used only with stationary objects.

[0008] It is highly desirable to provide an image capturing system thatwill capture three-dimensional images of objects that are notstationary, but which may move. It is also desirable that thethree-dimensional image has high resolution and high accuracy. It isparticularly desirable that the three-dimensional image captures thetotality of the object.

[0009] It is particularly desirable to provide an image capturing systemthat will have the ability to capture an accurate three-dimensionalimage of an infant's head. Capturing of such an image has not beenpossible with prior image capturing systems for a variety of reasons,one of which being that infants are not stationary for the times thatprior systems require to scan or capture the data necessary to produce athree-dimensional image. Another reason is that prior systems could onlyacquire a partial three-dimensional imager portion. The need for such asystem is for producing cranial remodeling bands is great.

[0010] Prior to the present invention, the process by which a cranialremodeling band is fabricated required obtaining a negative or ‘cast’impression of the child's head. The cast is obtained by first pulling acotton stockinet over the child's head, and then casting the head withquick setting, low temperature plaster.

[0011] The casting technique takes approximately 7 to 10 minutes. Afterthe initial casting, a plaster model or cast of the infant's head ismade and is used for the fabrication of the cranial remodeling band.

[0012] It is highly desirable to simplify the process by utilizingdigitization techniques to produce useful digital three-dimensionalimages of the entire head. We undertook an exhaustive search to identifyand evaluate different digitization techniques. Numerous laser scanning,structured light, Moire, and triangulated CCD camera systems wereevaluated and rejected as inadequate for one reason or another.

[0013] Prior digitization techniques and systems fail to recognize theparticular unique challenges and requirements necessary for a system forthe production of digital images of infant heads. The infant patientsrange in age from three to eighteen months of age. The younger infantsare not able to follow verbal instructions and are not able todemonstrate head control while the older infants are difficult tocontrol for more than a brief moment of time. A wide variety of headconfigurations, skin tone, and hair configurations also needed to becaptured. A digitization system must acquire the image in a fraction ofa second, i.e., substantially instantaneously, so that the child wouldnot need to be restrained during image capture, and so that movementduring image acquisition would not affect the data. The system datacapture must be repeatable, accurate and safe for regular repeated use.In addition, to be used in a clinical setting the system must be robust,easy to use, and easy to calibrate and maintain without the need forhiring additional technical staff to run the equipment. Imageacquisition, processing, and viewing of the data must be performed insubstantially real time in order to ensure that no data was missingbefore allowing the patient to leave the office.

[0014] Numerous existing digitization techniques were evaluated. Laserscanning methods have the disadvantage of the long time, typically 14-20seconds, that is required to scan an object. Because of the long time,an infant being scanned would have to be restrained in a specificorientation for the scan time. Recent advances in laser scanning haveproduced scan systems that can perform a scan in 1-2 seconds. Howevereven this scan rate is too slow for an unrestrained infant. The use oflasers also raises concerns regarding their appropriateness and safetyfor use with an infant population. While many prior digitization systemsuse ‘eye safe’ lasers, the use of protective goggles is still frequentlyrecommended.

[0015] Structured-light Moire and phase-shifted Moire systems used incertain 3D imaging systems are difficult to calibrate, are costly, andare relatively slow and therefore are not suitable for use in obtainingimages of infants. In addition these systems are incapable of capturingthe entirety of an object in one time instant.

[0016] Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) arenot particularly useful for the present application simply due to size,expense and concerns regarding radiation and the need to anesthetize theinfant.

[0017] Prior systems that rely solely on triangulation of digitalcameras proved to have insufficient accuracies, particularly as theobject being imaged varied in shape and size from a calibrationstandard.

[0018] Structured light systems that combined triangulated digitalcameras with a projected grid or line pattern can capture only onesurface at a time because the grids projected by multiple projectorsinterfered with each other resulting in a loss of data. In addition, theimages captured by this structured light systems need to be fit togetherlike a three-dimensional jigsaw puzzle, and required that markers beplaced on the subject in order to facilitate this registration process.

SUMMARY OF THE INVENTION

[0019] In accordance with the principles of the invention a digitizer isutilized to capture a three dimensional digital image of a deformed headto produce first digital data. The first digital data is utilized by acomputer to automatically provide cranial remodeling deviceconfiguration information for use in fabricating a cranial remodelingdevice for the deformed head.

[0020] In accordance with another aspect of the invention the methodincludes utilizing a neural network to generate configuration data.

BRIEF DESCRIPTION OF THE DRAWING

[0021] The invention will be better understood from a reading of thefollowing detailed description taken in conjunction with the drawingfigures in which like designations are utilized to identify likeelements, and in which:

[0022]FIG. 1 is a block diagram of an image capture system in accordancewith the invention;

[0023]FIG. 2 is a top view of a portion of the image capture system ofFIG. 1;

[0024]FIG. 3 is a cross-section take along lines 3-3 of the imagecapture system portion of FIG. 2;

[0025]FIG. 4 is a representation of a random infrared image projectedonto an object for which an image is to be captured;

[0026]FIG. 5 is a top view of the image-capturing portion of a secondembodiment of a portion of an image in accordance with the invention;

[0027]FIG. 6 is a planar view of an image-capturing module utilized inthe image-capturing portion shown in FIG. 5;

[0028]FIG. 7 is a flow diagram of a calibration operation of a system inaccordance with the invention;

[0029]FIG. 8 is a flow diagram of operation of a system in accordancewith the invention;

[0030]FIG. 9 is a detailed flow diagram of a portion of the flow diagramof FIG. 8;

[0031]FIG. 10 illustrates steps in a method in accordance with theprinciples of the invention;

[0032]FIG. 11 is a block diagram of a system in accordance with theprinciples of the invention;

[0033]FIG. 12 illustrates use of Principal Components Analysis andneural networks in the illustrative embodiment of the invention;

[0034]FIG. 13 illustrates steps in accordance with another aspect of theinvention;

[0035]FIG. 14 is table;

[0036]FIG. 15 represents a neural network;

[0037]FIG. 16 is a graph;

[0038]FIG. 17 is a block diagram of a system in accordance with theprinciples of the invention;

[0039]FIG. 18 is a block diagram of another system in accordance withthe principles of the invention;

[0040]FIG. 19 illustrates training a neural network in accordance withanother aspect of the invention;

[0041]FIG. 20 illustrates training a neural network in accordance withyet another aspect of the invention;

[0042]FIG. 21 illustrates a portion of the method utilized in the systemof FIG. 18;

[0043]FIG. 22 illustrates other aspects of the method utilized in thesystem of FIG. 18;

[0044]FIG. 23 illustrates a cast of a modified head with trim lines fora cranial remodeling band marked shown thereon;

[0045]FIG. 24 shows the trim lines of FIG. 22 without the cast; and

[0046]FIG. 25 illustrates a data base format in accordance with theprinciples of the invention.

DETAILED DESCRIPTION

[0047] Turning now to FIG. 1, a block diagram of an image capture systemor digitizer 100 is shown in block diagram form. System 100 includes aplurality of image capturing apparatus 101. Each image capturingapparatus is operable such that a three-dimensional image is capturedfor a surface portion of an object that is disposed within the field ofview of the image capturing apparatus.

[0048] The image capturing apparatus 101 are all coupled to andcontrolled by processing apparatus 105 via a bus 107. In additionprocessing apparatus 105 has associated with it program memory 109 anddata memory 111. It will be understood by those skilled in the art thatprocessing apparatus 105 may include one or more processors that arecommercially available from a wide variety of sources, such as the IntelPentium 4 or Itanium chip based processors. Program memory 109 and datamemory 111 may be the same memory, or each may comprise a plurality ofmemory units.

[0049] Program memory 109 includes an image-processing algorithm that isutilized to process digitized three-dimensional images of surfaceportions provided by image capturing apparatus 101 to produce adigitized image of the entirety of an object.

[0050] In operation, processor apparatus 105 controls image captureapparatus 101 such that all of image capture apparatus 101 aresimultaneously operated to capture digitized first images ofcorresponding surface portions of an object. The digitized first imagesare uploaded into data memory 111 under control of processor apparatus105.

[0051] Processor apparatus 105 operates on the digitized first imagesstored in memory 111 in accordance with the first algorithm stored inmemory 109 to produce a composite three-dimensional digitized image fromall of the first digitized images. The composite three-dimensionaldigital image is stored in memory 111 by processor 105. A display 113coupled to processor apparatus 105 may be used to display thethree-dimensional composite image of the object.

[0052] The plurality of image capturing apparatus 101 are arranged todefine a space 200 within which a three-dimensional image is captured ofan object 201. As shown in FIGS. 2 and 3 the image capturing apparatus101 are arranged to define a space 200 in the shape of a hemisphere.Although the illustrative embodiment defines a hemispherical shape, itwill be understood by those skilled in the art that the defined spacemay be of a different configuration. It should also be apparent to thoseskilled in the art that the principles of the invention are not limitedto the positioning of image capturing apparatus to any particular shapeobject 201. For certain objects 201, the image capturing apparatus maydefine a full sphere. In other implementations, the image capturingapparatus may define a space that is elongated in one or moredirections. It will also be apparent to those skilled in the art thatthe size of the space 200 will be determined by the characteristics ofthe plurality of image capturing apparatus.

[0053] The number and positioning of image capturing apparatus 101 areselected to achieve a predetermined accuracy and resolution. The imagecapture speed of the image capturing apparatus 101 is selected toprovide a “stop-action” image of the object 201. Thus, for example,conventional photographic speeds may be used to determine the top speedof an object 201 that moves within the space 200. To the extent that anobject 201 extends outside of space 200, that portion 201A of object 201that is within space 200 will be image captured such that the entiretyof that portion 201A that is within space 200 will captured as adigitized three-dimensional image.

[0054] In the illustrative embodiment of the invention, each imagecapturing apparatus 101 includes a plurality of digital cameras 102 suchas CCD (charge coupled device) cameras 102 and a projector 104. Each CCDcamera 102 is a high-resolution type camera of a type that iscommercially available. Each projector 104 projects a pattern onto theobject to facilitate processing of the images captured by the pluralityof digital cameras 102 within an image capturing apparatus 101 into athree-dimensional image of a corresponding portion of the object 201.Projector 104 projects a random infrared pattern 401 as shown in FIG. 4onto the object 201 that permits an algorithm to easily utilizetriangulation to generate a digitized three-dimensional representationof the corresponding portion of object 201.

[0055] The CCD cameras 102 and projectors 104 may be supported on one ormore supports such as the representative supports or support members301, 303 shown in FIG. 3.

[0056] A particularly useful application of digitizer 100 is for use incapturing three-dimensional images of the totality of an infant's head.Producing a three-dimensional image of an infant is particularlydifficult because infants do not remain motionless. Furthermore themotion that an infant may make is somewhat unpredictable. The infant maymove his or her head in one direction while tilting and rotating it. Themotion may be smooth or it may be jerky. The infant may move his head inone direction while rotating it in the opposite direction. It thereforeis important that the system operate at a speed to capture the entiretyof the infant's head in one instant. To provide a digitizer whichutilizes a safe and noninvasive method of obtaining a 3D model of aninfant's cranium, technological challenges had to be overcome that werenot immediately evident during the initial stages of development. To beuseful in a clinical setting, a digitizer must be fast, safe, accurate,repeatable, quiet, capture all skin tones, be impervious to motion, andnot require the child to be restrained in a specific orientation. To beuseful, the digitizer captures a 360° image which includes the face, topof the head, and lower occiput/neck region. A photographic image of thechild is acquired and can be seamlessly overlaid on thethree-dimensional display of the head to guarantee patientidentification. The digitized model is processed and visualized withinminutes to ensure that no data are missing before allowing the patientto leave the office. Calibration and operation of digitizer 100 issimple, fast, and robust enough to handle normal clinical operation.

[0057] Turning now to FIG. 5, one embodiment of digitizer 100 that isparticularly useful with infant head image capture comprises 18triangulated digital cameras 102. Cameras 102 are arranged onto threesupports or modules 501. Six cameras 102 are located in each module 501.Modules 501 are arranged in an equilateral triangle arrangement witheach module 501 located at a vertex. Twelve of the triangulated cameras102 are used to obtain digital image information regarding thethree-dimensional shape of the infant's head 201. The remaining sixcameras 102 capture digital photographs (i.e. texture data) of thechild. A single projector 104 is located in each of the three modules501, and projects a random infrared speckle pattern such as shown inFIG. 4 onto the child 201 at the moment the image is taken. This patterncannot be seen by the operator or the child, but is visible to the 12cameras 102 that obtain the digital shape information.

[0058] It is important that the digitizer is calibrated so that thedigital data accurately represents the object or infant having its imagecaptured. Turning to FIG. 7, calibration is accomplished by placing acalibration object into the center of the digitizer at step 701 and thenoperating all of cameras 102 simultaneously with projectors 104 tosimultaneously capture 12 images of the object at step 703. At step 705,using the 12 images, along with information about the calibrationstandard itself, the precise location and orientation of each digitalcamera 102 with respect to one another is determined. Data regardingeach of the camera's focal lengths obtained at step 707, and lensaberration information obtained at step 709 are recorded with thelocation and orientation data are recorded at step 711 in a calibrationfile. This calibration file is used later to reconstruct a 3D image ofthe child from 12 separate digital images.

[0059] To acquire the infant's image, a system operator first enters thepatient information into the digitizer 100 as indicated at step 801 ofFIG. 8. The infant is placed into position as indicated at step 803.Both the child 201 and parent are located in the center of theequilateral triangle with the infant sitting on an adjustable, rotatingstool. The infant 201 is supported by the parent, who may remain in thesystem while the child is digitized. The infant's head is not restrainedand may move in motion having pivotal, rotational and translationcomponents. When the parent and infant are in position the systemoperator actuates digitizer 100 to capture and simultaneously record 18images of the child at step 805. Within two and half minutes, imagesfrom the 12 shape cameras are reconstructed into a 360° digital modelusing the previously recorded calibration data. Texture data (i.e.digital photographs) are automatically overlaid on the model, althoughthe data may be viewed with or without this information. (FIGS. 3-6)Processing the 12 images into a single model can either be doneimmediately following the acquisition, or several images can be acquiredand processed at a later time. Preferably the image is displayed asindicated at step 807 and the image capture is verified at step 809. Theimage data of the obtained image is stored at step 811. If the imageobtained is not acceptable, new images may be captured, displayed andviewed.

[0060] Turning now to FIG. 9, the operation of digitizer 100 incapturing an image is shown in a more detailed flow diagram. At step901, image capture is initiated. Simultaneously, all projectors 104 areactuated at step 903 and all cameras 102 are operated at step 904. Theresulting digital images are downloaded from all of cameras 102 toprocessor 105 at step 907 and stored in memory 111 at step 909. The datafrom cameras 102 in a triangulation pair are processed in accordancewith a first algorithm in a program module from memory 109 at step 911to produce intermediate three-dimensional digital images ofcorresponding portions of the object or infant's head 201. Theintermediate three-dimensional digital images are stored in memory 111at step 913. Processor 105 then processes the intermediatethree-dimensional images at step 915 in accordance with a secondalgorithm in a program module from memory 109 to produce a completethree-dimensional digital image file for the whole or entire object thatis within space 200 or the infant's whole or entire head 201 withinspace 200. Processor 105 stores the entire three-dimensional image filein memory 111 for later use.

[0061] Accuracy is often reported as a ‘mean’ or ‘average’ differencebetween the surfaces, however in this situation reporting an average isinaccurate because the surface created from the new data set may havecomponents that lay both above (+) and below (−) the reference surface.These positive and negative values offset each other resulting in a meanvalue around zero. In situations where this cancellation can occur, itis necessary to report the mean difference as a Root Mean Square (RMS).The root mean square statistic reports typical magnitudes of deviationswithout regard for positive or negative values.

[0062] By using a best-fit analysis type algorithm to analyze theillustrative digitizer, the RMS mean deviation between the surfaces wascalculated to be +/−0.236 mm, with over 95% of the data clearly fallingwithin +/−0.5 mm.

[0063] A hazard analysis performed on the system of the inventiondemonstrates that, system 100 is safe. Digitizer 100 will not causeretinal blue-light or infrared eye injuries.

[0064] One advantage of digitizer 100 is that the image acquisition isfast enough so that motion of the infant does not present a problem forimage capture, or affect the accuracy of the data acquired. If the imagecould not be captured ‘instantaneously’ it would be necessary to fixtureor restrain the child in one position in order to ensure there would beno motion artifacts in the data.

[0065] Capture of all 18 images (12 shape, 6 texture) is accomplishedthrough utilization of an interface 103 in FIG. 1 that functions singleframe grabber circuit board. At image capture time processor 105generates a signal via interface 103 that is sent out to all cameras 102to simultaneously record the digital images for processing. Each camera102 records a digital image at a speed of {fraction (1/125)}^(th) of asecond (0.008 seconds). This nearly instantaneous capture has allowed usto capture digitized images of infants in motion. The symmetricalplacement of the cameras around the periphery also ensures that thechild's specific orientation and position within the space 200 is not afactor.

[0066] Post-processing of intermediate images into a single digitalmodel is done quickly so that the complete image can be reviewed beforeallowing the patient to leave the office. In an illustrative embodimentof the system the complete image may be produced in less than threeminutes

[0067] Once processed, the data may be viewed in a variety of formatsthat include point cloud, wire frame, surface, and texture. As the nameimplies, the image presented as a point cloud consists of hundreds ofthousands of independent single points of data. A wire frame, sometimesreferred to as a polygon or triangulated mesh, connects three individualdata points into a single polygon with each data point being referred toas a vertex. A wire frame is the first step in viewing the individualdata points as one continuous connected ‘surface’. Once connected as aseries of polygons, mathematical algorithms are applied to convert thefaceted, polygonized surface into a smooth continuous surface upon whichmore complex measurements and mathematical analyses can be performed.While point cloud, wire frame and surface rendering are the most commonmethods for viewing digital data, it is also possible to obtain textureinformation which is seamlessly overlaid on the model. Texture data isoverlaid onto the digital image to ensure proper patient identification.

[0068] The projection of a random infrared pattern by projectors 104,rather than a grid or line pattern, overcomes problems with interferenceand enables digital capture of the entire infant head or object 201 in asingle shot. This includes a 360° image including the face, top of thehead, and neck/occipital region all acquired within 0.008 seconds.Digitizer 100 is safe, impervious to motion, does not require the infantto be sedated or restrained, and images can be viewed within 2-3 minutesof acquisition. The digital data can be exported to create physicalmodels using stereo lithography or carved on a 5-axis milling machine.Quantitative data (linear and surface measurements, curvature, andvolumes) can also be obtained directly from the digital data.

[0069] The three-dimensional images are stored in memory 111 ofdigitizer 100 as shown in FIG. 1. A sequence of three-dimensional imagesmay be captured and stored in memory 111 for later playback. Thethree-dimensional images may be sequentially displayed to produce athree-dimensional movie of the infant or object in motion. A particularfeature is that since each three-dimensional image is taken of theentirety of the infant's head or object, the view of the image onplayback may be changed to observe different portions of the infant'shead or object as it moves. The view may be taken from any point on theexterior of the image capture space defined by the digital cameras.

[0070] Turning now to FIG. 10, steps 1001 and 1003 are steps that wereutilized in the past to produce a custom cranial remodeling device orband for an infant with a deformed head. A positive life size cast ismade of an infant's head or a first shape as indicated at 1001. Acorresponding modified cast or second shape is then made from which acranial remodeling band is produced at step 1003. In the past, a cranialremodeling band for the infant is produced by forming the band on thesecond cast which represents a modified head shape. A library ofhundreds of infant head casts and corresponding modified casts has beenmaintained at the assignee of the present invention and this library ofactual head casts and the corresponding modified casts is believed to bea unique resource. It is this unique resource that is utilized toprovide databases for developing the method and apparatus of the presentinvention.

[0071] An additional unique resource is that databases of additionalinformation corresponding to each infant have been developed by theassignee of the present invention. That database includes informationthat identifies the type of cranial remodeling device for each infanthead shape as well as the style of the cranial remodeling device andfeatures selected for incorporation into the cranial remodeling deviceto provide for appropriate suspension and correction of the deformity.Still further, each cranial remodeling device has trim lines that areuniquely cut so as to provide for appropriate suspension andfunctionality as well as appearance. A further database developed by theassignee of the present invention has trim line data for each cranialremodeling device that has been previously fabricated corresponding tothe casts for unmodified heads.

[0072] In a first embodiment of the invention, shown as system 1200 inFIG. 11, the databases 1203, 1205 of unmodified shapes and correspondingmodified shapes are used by a computer 1201 to train a neural network1300.

[0073] In accordance with one aspect of the invention, each unmodifiedor first head shape is digitally captured at step 1005 as shown in FIG.10 by a digitizer 100 shown in FIG. 11, to produce first digital data atstep 1007 to provide a complete three dimensional representation of theentirety of a head including the top portion. The first digital data isstored in database 1203 at step 1009. Each corresponding modified orsecond head shape is digitally captured by digitizer 100 at step 1011 toproduce second digital data at step 1013. The second digital data isstored in database 1205 at step 1015. One to one correspondence isprovided between each first digital data and the corresponding seconddigital data as indicated at step 1017. The correspondence betweendigital data for first and corresponding second head shapes ismaintained by utilizing any one of several known arrangements formaintaining correspondence.

[0074] In accordance with one aspect of the illustrative embodiment thefirst and second data stored comprises Cartesian coordinates for aplurality of points on the surface of the corresponding shape.

[0075] In the illustrative embodiment shown in FIG. 10, digitizer 100utilizes a plurality of digital cameras that are positioned tosubstantially surround the entirety of a cast or a patient's head suchthat a substantially instantaneous capture of the cast or of theinfant's head is obtained. As used herein, the term “digitizer” isutilized to identify a data capture system that produces digital datathat represents the entirety of a cast or a head and which is obtainedfrom a substantially instantaneous capture.

[0076] In accordance with an aspect of the illustrative embodiment ofthe invention, neural network 1300 is “trained”, as explained below, sothat captured data of a first or unmodified shape 1301 shown in FIG. 12which has no corresponding second or modified shape is processed byneural network 1300 to produce a second or modified shape 1303. Morespecifically, principal components analysis (PCA) is utilized inconjunction with neural network 1300. Neural network 1300 is trained byutilizing captured data for first shapes from database 1203 withcorresponding captured data for second shapes from database 1205.

[0077] Turning to FIG. 13, operation of system 1200 is shown. At steps1401 and 1403, one or more databases are provided to store data for aplurality of first or unmodified captured shapes and to store data for aplurality of corresponding second or modified captured shapes.

[0078] The data from each captured first and second image is representedusing the same number of data points. In addition, all captured imagesare consistently aligned with each other.

[0079] The captured data for all head shapes represented in thedatabases 1203, 1205 of are aligned in a consistent way. The consistentalignment or image orientation has two separate aspects: alignment ofall captured modified images with each other as shown at step 1405; andalignment of each unmodified captured image with the correspondingmodified captured image as shown at step 1407. Alignment of unmodifiedand modified captured images ensures that the neural network willconsistently apply modifications. Alignment of the modified capturedimages with one another allows PCA to take advantage of the similaritiesbetween different cast shapes.

[0080] The casts from which the captured images are obtained do notinclude facial details. Typically the face portion is merely a plane.The position of the face plane is really a result of deformity. To alignthe shapes a manually iterative visualization process is utilized. Thisapproach “solved” half of the alignment issue—aligning all of themodified images with each other so that the principal componentsanalysis could take advantage of the similarities between these shapes.

[0081] To align each unmodified captured image with its correspondingcaptured modified image, alignment of the face planes is was utilized.For the most part, the face planes represent a portion of the shapesthat are not modified from the unmodified to the modified head shape andprovide consistency. An additional attraction to this approach is thatthere on the head actual casts, there is writing on the face planes andthis writing is visible in the texture photographs that can be overlaidonto the captured images. Alignment of face planes and writing providesa precise registration of the unmodified and modified captured images.

[0082] An automated approach to this alignment was developed usingseveral of the first captured images. The automated alignment workswhere texture photographs are well focused and clear. In other casesautomated alignment is supplemented with alignment by selecting“freehand” points on both the modified and unmodified images usingcommercially available software and then using the registration tool ofthat software. This approach aligned all unmodified captures withmodified captures so that corrections would be consistently applied.

[0083] The capture data for both the unmodified and modified head shapesare normalized at step 1409 for training the neural network. As part ofthe normalization, a scale factor is stored for each for each normalizedhead or shape set.

[0084] As indicated at step 1411, PCA is utilized with the alignedshapes to determine PCA coefficients. Because PCA uses the same set ofbasis vectors (shapes) to represent each head (only the coefficients inthe summation are changed), each captured image is represented using thesame number of data points. For computational efficiency, the number ofpoints should be as small as possible.

[0085] The original digitized data for first and second shapes stored indatabases 1203, 1205 represent each point on a surface using athree-dimensional Cartesian coordinate system. This approach requiresthree numbers per point; the x, y, and z distances relative to anorigin. In representing the head captures, we developed a scheme thatallows us to represent the same information using only one number perdata point. Mathematically the approach combines cylindrical andspherical coordinate systems. It should be noted that to obtain threedimensional representations of an object, a spherical coordinate systemmay be utilized. However, in the embodiment of the invention, the bottomof the shape is actually the neck of the infant, and is not of interest.

[0086] Conceptually this approach is similar to a novelty toy known as“a bed of nails.” Pressing a hand or face against a grid of moveablepins pushes the pins out to form a 3D copy on the other side of the toy.This approach can be thought of as a set of moveable pins protrudingfrom a central body that is shaped like a silo—a cylinder with ahemisphere capped to the top. The pins are fixed in their location,except that they can be drawn into or out of the central body. Usingthis approach, all that is needed to describe a shape is to give theamount that each pin is extended.

[0087] To adequately represent each head shape, a fixed number ofapproximately 5400 data points are utilized. To represent each of thecaptures using a fixed number of data points, the distance that each“pin” protrudes is computed. This is easily achieved mathematically bydetermining the point of intersection between a ray pointing along thepin direction and the polygons provided by data from the infraredimager. A set of such “pins” were selected as a reasonable compromisebetween accuracy of the representation and keeping the number of pointsto a minimum for efficiency in PCA. The specific set of points wascopied from one of the larger cast captures. This number was adequatefor a large head shape, so it would also suffice for smaller ones. Acommercially available program used to execute this “interpolation”algorithm requires as input the original but aligned capture data andprovides as output the set of approximately 5400 “pin lengths” thatrepresent the shape.

[0088] Using the consistent alignment and the consistent array of datapoints described above, the normalized data for each captured cast wasinterpolated onto this “standard grid.” Computing the covariance matrixproduced an n×n matrix, where “n” is the number of data points. As thename implies, this covariance matrix analyzes the statisticalcorrelations between all of the “pin lengths.” Computing the eigenvaluesand eigenvectors of this large covariance matrix provides PCA basisshapes. The PCA shapes are the eigenvectors associated with the largest64 eigenvalues of the covariance matrix. This approach of computingbasis shapes using the covariance matrix makes optimal use of thecorrelations between all the data points used on a standard grid.

[0089] PCA analysis allows cast shapes to be represented using only 64PCA coefficients. FIG. 14 sets out the hyper parameters for the 64 PCAcoefficients. To transform the unmodified cast shapes into correctmodified shapes, it is only necessary to modify the 64 PCA coefficients.For this processing task we selected and provide neural network 300 asindicated at step 413 of FIG. 4.

[0090] Neural networks are an example of computational tools known as“Learning Machines” and are able to learn any continuous mathematicalmapping.

[0091] As those skilled in the art will understand, learning machinessuch as neural networks are distinguished from expert systems, in whichprogrammers are utilized to program a system to perform the samebranching sequences of steps that a human expert would perform. Ineffect, an expert system is an attempt to clone the knowledge base of anexpert, whereas, a neural network is taught to “think” or operate basedupon results that an expert might produce from certain inputs.

[0092]FIG. 15 shows a conceptual diagram of a generic neural network1300. At a high level, there are three elements of a neural network: theinputs, {acute over (α)}₁-{acute over (α)}_(n), the hidden layer(s)1603, and the outputs β₁-β_(n). A neural network 1300 operates on inputs1601 using the hidden layer to produce desired outputs 1605. This isachieved through a process called “training.”

[0093] Neural network 1300 is constructed of computational neurons eachconnected to others by numbers that simulate strengths of synapticconnections. These numbers are referred to as “weights.”

[0094] Training refers to modification of weights used in the neuralnetwork so that the desired processing task is “learned”. Training isachieved by using PCA coefficients for captured data representative ofunmodified casts of infant heads as inputs to the network 1300 andmodifying weights of hidden layers until the output of the neuralnetwork matches PCA coefficients for the captured data representative ofcorresponding modified casts. Repeating this training thousands of timesover the entire set of data representing the unmodified andcorresponding captured shapes produces a neural network that achievesthe desired transformation as well as is statistically possible. In theillustrative embodiment, several hundred pairs of head casts wereutilized to train neural network 1300 at step 1415.

[0095] Testing on additional data from pairs of casts that the neuralnetwork was not trained with, or “verification testing”, was utilized inthe illustrative embodiment to ensure that the neural network 1300 haslearned to produce the appropriate second shape from first shapecaptured data and has not simply “memorized” the training set. Once thistraining of neural network 1300 is complete, as measured by the averageleast squares difference between the PCA coefficients produced by thenetwork and those from the modified cast shapes, the PCA coefficientweights are “frozen” and the network is simply a computer program likeany other computer program and may be loaded onto any appropriatecomputer.

[0096] A commercially available software toolbox was used to develop thelearning machine. The particular type of learning machine produced iscalled a Support Vector Machine (SVM), specifically a Least SquaresSupport Vector Machine (LS-SVM). Just like the neural networks describedabove, the SVM “learns” its processing task by modifying “weights”through a “training” process. But in addition to weights, the LS-SVMrequires a user to specify “hyper parameters.” For the radial basisfunction (RBF) type of LS-SVM used in this work, there are two hyperparameters: a (gamma) and 6 (sigma). The relative values of theseparameters control the smoothness and the accuracy of the processingtask. This concept is very similar to using different degree polynomialsin conventional curve fitting.

[0097]FIG. 16 shows a curve-fitting task in two dimensions for easyvisualization. Because there are a finite number of data points, (x,y)-pairs, it is always possible to achieve perfect accuracy by selectinga polynomial with a high enough degree. The polynomial will simply passthrough each of the data points and bend as it needs to in between thedata where its performance is not being measured. This approach providesridiculous results in between the data points and is not a desiredresult. One solution to this problem is to require that the curvedefined by the polynomial be smooth i.e., to not have sharp bends. Thissolution is fulfilled in the LS-SVM using ã and ó. Higher values of ócorrespond to smoother curves and higher values of ã produce greateraccuracy on the data set.

[0098] An unmodified or first shape represented by captured data isprocessed utilizing a principal components analysis algorithm. Theresulting PCA representation is processed by a neural network 1300 toproduce a second or modified PCA representation of a modified shape.

[0099] In the system of the illustrative embodiment of the inventioncross-validation was used to choose the hyper-parameters. Data israndomly assigned to four groups. Three of the four groups were used totrain the LS-SVM, and the remaining set was used to measure theperformance in predicting the PCA coefficients for the modified headshapes. In turn, each of the four groups serves as the test set whilethe other three are used for training. The group assignment/division wasrepeated two times, so a total of eight training and test sets wereanalyzed (four groups with two repetitions). This process was repeatedfor a grid of (ã, ó)-pairs ranging from 0-200 on both variables. Therange was investigated using a 70×70 grid of (ã, ó)-pairs, so a total of4900 neural nets were tested for each of the first 38 PCA coefficients.From this computationally intensive assessment, hyper-parameters weredetermined and validated for the first 38 PCA coefficients andextrapolated those results to select hyper-parameters for the remaining26. Further “tuning” of the remaining 26 PCA hyper-parameters isunlikely to produce significant improvement in the final results becausethe first PCA coefficients are the most influential on the solution. Thetable shown in FIG. 14 presents the results for the hyper-parametertuning.

[0100] Once hyper parameters were tuned, the LS-SVM models generallyproduced errors of less than two percent for the PCA coefficients of themodified casts in test sets (during cross-validation). Applying thesetuned models to head casts that were not part of the cross-validation ortraining sets also generated excellent results.

[0101] There is a surprising variability of the head shapes asrepresented by cast shapes. Modified casts are not simply small changesto a consistent “helmet shape.” Each is uniquely adapted to theunmodified shape that it is intended to correct. Being so stronglycoupled to the unmodified shapes makes these modified casts surprisinglydifferent from one another. Of the several hundred casts that weanalyzed, each is unique.

[0102] Interpolating the aligned shapes also provided surprises andchallenges. The “bed of nails” concept is very effective in reducing thesize of the data sets and providing a consistent representation for PCA.It helps reduce the number of data sets that would have otherwise beenrequired to train a larger neural network. Instead of representing eachdata point by three components, each data point is represented by onecomponent thereby reducing the number of data sets significantly. Byutilizing this approach, the process is speeded up significantly.

[0103] Returning to FIG. 13, at step 1415, neural network 1300 istrained as described above. Once neural network 1300 is trained, it isthen utilized to operate on new unmodified heads or shapes to produce amodified or second shape as indicated at step 1417.

[0104] Turning now to FIG. 17, a block diagram of a system 1800 inaccordance with the principles of the invention is shown. System 1800 isutilized to both train a neural network 1300 described above and then toutilize the trained neural network 1300 to provide usable modified headshapes from either casts of deformed head shapes or directly from suchan infant's head.

[0105] System 1800 includes a computer 1801 which may any one of anumber of commercially available computers. Computer 1801 has a display1823 and an input device 1825 to permit visualization of data andcontrol of operation of system 1800.

[0106] Direct head image capture is a desirable feature that is providedto eliminate the need to cast the children's head. An image capturingdigitizer 100 is provided that provides substantially instantaneousimage captures of head shapes. The digitized image 1821 a is stored in amemory 1821 by computer 1801. Computer 1801 utilizes a data conversionprogram 1807 to normalize data, store the normalized data in memory 1821and its scaling factor, and to convert the normalized, captured data to“bed of nails” data as described above. Computer 1801 stores themodified, normalized data 1821 b in memory 1821. Computer 1801 utilizingan alignment program 1809 to align modified data 1821 b to an alignmentconsistent with the alignments described above and to store the aligneddata in an unmodified shapes database 1803. Computer 1801 obtains PCAcoefficients and weightings from a database 1813 and utilizes neuralnetwork 1300 and a support vector machine 1817 to operate on the datafor a first shape stored in memory 1821 a to produce data for a modifiedor second shape that is then stored in memory 1821 b. The data for themodified shape stored in memory 1821 b may then be utilized to fabricatea cranial remodeling device or band for the corresponding head.

[0107] In another embodiment of the invention, shown in FIG. 18, asystem 1900 is also “trained” such that in addition to producing amodified head shape that is utilized for fabrication of a cranialremodeling band, system 1900 additionally determines a type and style ofthe cranial remodeling device or band which is particularly appropriatefor the deformity as well as a configuration for the device or band. Thetype and style of device is determined, in part, from the nature andextent of the cranial deformity and/or from the fit and function of thecranial remodeling band to correct the cranial deformity.

[0108] The type of remodeling device or band is selected based upon thenature of the deformity. In the illustrative embodiment of theinvention, the band types may be classified as Side-Opening, BrachyBand® or Bi-Cal™.

[0109] The DOC Band® or side-opening type is used primarily to treatchildren with plagiocephaly, or asymmetrical head configurations. Itapplies forces in a typically diagonal fashion. A representativeside-opening band is described in U.S. Pat. No. 5,094,229 which isincorporated herein by reference.

[0110] The Brachy Band® is used to treat brachycephaly, or deformationswhere the head is too wide, and too short. It applies forces on thelateral prominences and encourages growth of the head in length. Thisreturns the head to a more normal cephalic index (length to widthratio). An example of a Brachy Band is shown in U.S. Pat. No. Re 36,583which is incorporated herein by reference.

[0111] The Bi-Cal™ type is used to treated scaphocephaly, ordeformations where the head is too long, and too narrow. It appliesforces on the forehead and back of the head and encourages growth of thehead in width. This returns the head to a more normal cephalic index(length to width ratio)

[0112] In the illustrative embodiment of the invention, the style ofband or device includes: RSO or right side opening cranial remodelingband; WRSO or wide right side opening cranial remodeling band; LSO orleft side opening cranial remodeling band; WLSO or wide left sideopening cranial remodeling band.

[0113] The configuration of the devices is selected to provide specificfunctional attributes. Examples of such attributes include: suspension;application of corrective forces; and protection. Suspension refers tothose design configuration features that help to maintain the band inits proper orientation so that the corrective forces are applied wherethey need to be. In some cases, the design features themselves are usedto apply corrective forces which could not be achieved without theirinclusion. In some cases, the features are there to protect a surgicalsite. An example of this would be a strut that goes over the top of thehead in the bi-cal band.

[0114] Typical features include: use of anterior corners (unilateral orbilateral), posterior corners (unilateral or bilateral), fractionalanterior tops, fractional posterior tops, opposing corners, struts, orvarious combinations of each. It is not possible to genericallycategorize each feature, e.g., anterior corner, ¼ posterior top, etc, asonly used to provide a single function. In some instances, the featuresare multi-functional, for example providing both suspension and acorrective force.

[0115] In the illustrative embodiment of the invention, the “features”include standardized structural configurations which are referred to as:RAC or right anterior corner; LAC or left anterior corner; RPC or rightposterior corner; LPC or left posterior corner; a fractional or noanterior or posterior cap; and a partial or full strut across the top ofband.

[0116] The features may be combined in any number of combinations, butthe most common would be what is referred to as an “opposing corners”combination. An example of this would be a right side opening band thatalso has both a right anterior corner (RAC) and a left posterior corner(LPC). These features are not for aesthetics, but rather representfunction improvements to the band for both suspension and application ofcorrective forces.

[0117] It is difficult, if not impossible, to identify what type ofcranial remodeling device or band and its features should be for apatient if only information of the corrected shape is provided.

[0118] In the past, the type of cranial remodeling device or band, andthe additional features that should be incorporated were determined inthe past during the modification process. Thus when a head cast is beingmodified to produce a modified cast, a determination is made as to boththe necessary style of the band to be used and the features that shouldbe incorporated into the band. Both the selection of the type of deviceor band as well as the features to be incorporated are a function ofboth the original deformity as well as the corrected head shape. Thefeatures selected are not independent.

[0119] In system 1900, a database 1901 includes for each ummodified headshape dataset data identifying the type and style of cranial remodelingdevice as well as configuration features.

[0120] In system 1900, neural networks 1915 include neural network 1300trained to provide modified shape data from unmodified shape data asdescribed with respect to system 1800. In addition neural networks 1915includes neural network 1916 that is trained to select a type and styleof cranial remodeling device and to select a configuration of thecranial remodeling device.

[0121] Turning now to FIG. 25, the type, style and configuration dataentry format stored in database 1901 for each head is shown. Eachdatabase entry includes a reference file number to assist in correlatingto the corresponding head shape in database 1803. Each database entryalso includes an entry field for a band type, an entry field for a bandstyle, four entry fields for each of the right and left anterior cornersand right and left posterior corners, a field for identification of afractional anterior top, a field for identification of a fractionalposterior top, and a field for identification of a strut.

[0122] The methodology for training the neural networks 1916 is similarto that used in the first embodiment. Turning now to FIG. 19, data forunmodified head shapes obtained from database 1203 and correspondingtype, style and configuration data for cranial remodeling devices fromdatabase 1901 are utilized to train neural network 1916 such that neuralnetwork 1916 will automatically select the type, style and configurationdata for a cranial remodeling device.

[0123] By providing neural networks 1915 trained to generate datarepresentative of a corrected head shape and to select a correspondingcranial remodeling band and features, a highly automated cranialremodeling band fabrication system is provided by system 1900.

[0124] System 1900 includes a milling machine 1903 that receives datafrom computer 1801 and mills a model. Milling machines are commerciallyavailable that will receive digital data from a computer or otherdigital data source and which produce a milled three dimensional object.In system 1900 milling machine 1903 is one such commercially availablemilling machine. In operation system 1900 instantaneously captures threedimensional image data of an infant's head utilizing digitizer 1819.Computer 1801 stores the captured data 1821 a in memory 1821. Computer1801 then utilizes data conversion module 1807 to convert the data intoa “bed of nails” equivalent data and to provide alignment of thecaptured image and to restore the converted and aligned data in memory1821. Computer 1801 utilizes neural network 1300 in conjunction with theconverted and aligned data of the captured image to producecorresponding three dimensional image data for a modified or second headshape. The three dimensional data 1821 b for the modified or second headshape is stored in memory 1821. In addition, neural network 1916 is alsoutilized to select a corresponding cranial remodeling band type, styleand configuration which are likewise stored in memory 1821.

[0125] Computer 1801 then utilizes the three dimensional data 1821 b forthe modified shape to command and direct milling machine 1903 to producean accurate three dimensional model of the modified shape represented bydata 1821 b.

[0126] Computer 1801 also retrieves corresponding cranial remodelingband type, style and configuration features which are displayed ondisplay monitor 1823 to assist in fabricating a cranial remodeling bandfor the infant whose head was digitally captured.

[0127] In another embodiment of the invention, once the milling machine1903 has produced a three dimensional representation of a modified headbased upon data provided by computer 1801, a copolymer shell is vacuumformed on the representation of the head. The copolymer shell is thenshaped to produce the particular device type, style and configuration ina further digital controlled machine 1907.

[0128] To summarize, an infant having a deformed head that requirestreatment with a cranial remodeling band may have his or her head shapedigitally captured by system 1900. System 1900 may be utilized toautomatically produce a three dimensional representation of the infant'shead shape modified to produce a cranial remodeling band to correct forthe head deformities. System 1900 further may be operated toautomatically provide an operator with information pertaining to theappropriate configuration and features to be provided in the cranialremodeling band.

[0129] In accordance with yet another aspect of the present invention,in addition to data relating to the band type, style and configurationfeatures, the data may include data for trim lines for cranialremodeling bands. A neural network 1918 included with the neuralnetworks is trained in the same manner that neural network 1916 istrained with trim line data as illustrated in FIG. 20. By including datafor trim lines, in database 1901A and utilizing neural network 1916 toalso learn the trim lines to be utilized in various cranial remodelingbands, system 1900 produces a cranial remodeling band of an appropriateconfiguration and having appropriate features, and appropriate trimlines all without any significant human intervention.

[0130]FIG. 21 illustrates the method of storing cranial remodelingdevice type style and feature data and trim line data. FIG. 21 issimilar to the flow diagram of FIG. 10 with the additional step ofstoring in a database cranial remodeling device type, style andconfiguration feature data corresponding to first digital data at step1019. FIG. 21 also illustrates the step of storing in a database trimline data for cranial remodeling devices corresponding to first digitaldata at step 1021.

[0131] Neural network 1916 is thus trained to automatically generatetrim lines. Once generated, trim line data may be utilized to actuallymill trim lines right onto the copolymer cranial band vacuum formed ontothat three dimensional representation of a modified head eitherutilizing milling machine 1903 or machine 1907. Machine 1907 may be alaser trimming machine.

[0132]FIGS. 23 and 24 illustrate exemplary trim lines for a cranialremodeling band. In FIG. 23 trim lines 2301, 2303 are shown on amodified head shape 2300. To better see the trim lines 2301, 2303, FIG.23 shows the trim lines 2301, 2303 for the cranial remodeling bandwithout head shape 2300. Trim line 2301 illustrates the lower margin ofcranial remodeling device or band for head shape 2300 and trim line 2303illustrates the upper margin of the cranial remodeling band.

[0133]FIG. 22 illustrates the method of training system 1900. The methodof FIG. 22 is similar to the method of FIG. 13. At step 2201 a databaseof cranial remodeling device type, style and configuration feature datais provided. At step 2203 a database of trim line data is provided. Atstep 2205 a neural network is trained to select cranial remodelingdevice type, style and configuration features. At step 2207, a neuralnetwork is trained to select trim lines for the cranial remodelingdevice. At step 2209, the trained neural networks are utilized toproduce a cranial remodeling device.

[0134] It will be appreciated by those skilled in the art that a system1900 in accordance with the principles of the invention may beconfigured to automate various aspects of producing cranial remodelingdevices automatically and ranging from automatic production of threedimensional representations of head shapes modified to shapes thatpermit the forming of an appropriate cranial remodeling band toautomatic production of cranial remodeling bands to be utilized in thecorrection of head shape abnormalities.

[0135] The invention has been described in terms of illustrativeembodiments. It will be apparent to those skilled in the art thatvarious changes and modifications can be made to the illustrativeembodiments without departing from the spirit or scope of the invention.It is intended that the invention include all such changes andmodifications. It is also intended that the invention not be limited tothe illustrative embodiments shown and described. It is intended thatthe invention be limited only by the claims appended hereto.

What is claimed is:
 1. A method for producing cranial remodeling devicesto correct for cranial shape abnormalities comprising: capturing a threedimensional digital image of a deformed head to produce first digitaldata; and utilizing said first digital data to automatically providecranial remodeling device information for use in fabricating a cranialremodeling device for said deformed head.
 2. A method in accordance withclaim 1, wherein: said cranial remodeling device information comprisesidentification of one of a plurality of types of cranial remodelingdevices.
 3. A method in accordance with claim 2, wherein: said pluralityof types of cranial remodeling devices includes devices for treatment ofspecific types of cranial deformities.
 4. A method in accordance withclaim 3, wherein: said specific types of cranial deformities compriseone or more of plagiocephaly, brachycephaly, and scaphocephaly.
 5. Amethod in accordance with claim 3, wherein: said cranial remodelingdevice information comprises predetermined configuration features thatmay be incorporated in said cranial remodeling device.
 6. A method inaccordance with claim 5, wherein: said predetermined configurationfeatures comprise predetermined design features.
 7. A method inaccordance with claim 6, wherein: said predetermined design features areselected from a group comprising a right anterior corner, a leftanterior corner, a right posterior corner, a left posterior corner, afractional posterior cap, a fractional anterior cap and a lengthwisestrut across top of band.
 8. A method in accordance with claim 1,wherein: said cranial remodeling device information comprises a styleinformation selected from a group comprising a right side openingcranial remodeling band, a wide right side opening cranial remodelingband, a left side opening cranial remodeling band, and a wide left sideopening cranial remodeling band.
 9. A method in accordance with claim 1,comprising: utilizing said first data to automatically produce aphysical model; and utilizing said physical model and said cranialremodeling device information to produce said cranial remodeling device.10. A method for producing cranial remodeling devices to correct forcranial shape abnormalities comprising: capturing a three dimensional,substantially global digital image of a deformed head to produce firstdigital data; and utilizing one or more neural networks operating onsaid first digital data to automatically provide cranial remodelingdevice information for use in fabricating a cranial remodeling devicefor said deformed head.
 11. A method in accordance with claim 10,wherein: said cranial remodeling device information comprisesidentification of one of a plurality of types of cranial remodelingdevices.
 12. A method in accordance with claim 11, wherein: saidplurality of types of cranial remodeling devices includes devices fortreatment of specific types of cranial deformities. 13 A method inaccordance with claim 12, wherein: said specific types of cranialdeformities comprise one or more of plagiocephaly, brachycephaly, andscaphocephaly.
 14. A method in accordance with claim 12, wherein: saidcranial remodeling device information comprises predetermined designfeatures to be incorporated in said cranial remodeling device.
 15. Amethod in accordance with claim 14, wherein: said predetermined designfeatures comprise standardized structural configurations.
 16. A methodin accordance with claim 15, wherein: said standardized structuralconfigurations are selected from a group comprising a right anteriorcorner, a left anterior corner, a right posterior corner, a leftposterior corner, a fractional anterior cap, a fractional posterior cap,and a lengthwise strut across top of band.
 17. A method in accordancewith claim 10, wherein: said cranial remodeling device informationcomprises a device style selected from a group comprising a right sideopening cranial remodeling band, a wide right side opening cranialremodeling band, a left side opening cranial remodeling band, and a wideleft side opening cranial remodeling band.
 18. A method in accordancewith claim 10, comprising: utilizing said first data to automaticallyproduce a physical model of a modified head shape; and utilizing saidphysical model of said head and said cranial remodeling deviceinformation to produce said cranial remodeling device.
 19. A method forproducing a cranial remodeling device to correct for a cranial shapeabnormality, comprising: capturing a digital image of a deformed head toproduce first digital data; automatically processing said first digitaldata to produce second data corresponding to a desired shape for use informing a cranial remodeling device; and automatically providing cranialremodeling device information for use in fabricating a cranialremodeling device for said deformed head.
 20. A method in accordancewith claim 19, wherein: said cranial remodeling device informationcomprises identification of one of a plurality of types of cranialremodeling devices.
 21. A method in accordance with claim 20, wherein:said plurality of types of cranial remodeling devices includes devicesfor treatment of specific types of cranial deformities.
 22. A method inaccordance with claim 21, wherein: said specific types of cranialdeformities comprise one or more of plagiocephaly, brachycephaly, andscaphocephaly.
 23. A method in accordance with claim 21, wherein: saidcranial remodeling device information comprises predetermined designfeatures that are selectable for inclusion in said cranial remodelingdevice.
 24. A method in accordance with claim 23, wherein: saidpredetermined design features comprise standardized structuralconfigurations.
 25. A method in accordance with claim 24, wherein: saidstandardized structural configurations are selected from a groupcomprising two or more of a right anterior corner, a left anteriorcorner, a right posterior corner, a left posterior corner, a fractionalanterior cap, a fractional posterior cap, and a lengthwise strut acrosstop of band.
 26. A method in accordance with claim 19, wherein: saidcranial remodeling device information is automatically selected from agroup comprising a right side opening cranial remodeling band, a wideright side opening cranial remodeling band, a left side opening cranialremodeling band, and a wide left side opening cranial remodeling band.27. A method in accordance with claim 19, comprising: utilizing saidsecond data to automatically produce a physical model of said desiredshape; and utilizing said physical model and said cranial remodelingdevice information to produce said cranial remodeling device.
 28. Amethod in accordance with claim 19, comprising: utilizing said seconddata to automatically produce said cranial remodeling device.
 29. Asystem for producing cranial remodeling devices to correct for cranialshape abnormalities comprising: a digitizer operable to capture threedimensional digital image data of a deformed head to produce firstdigital data; a computer; computer programs operable on said computersuch that said computer processes said first digital data toautomatically provide cranial remodeling device information for use infabricating a cranial remodeling device for said deformed head.
 30. Asystem in accordance with claim 29, wherein: said cranial remodelingdevice information comprises identification of one of a plurality oftypes of cranial remodeling devices.
 31. A system in accordance withclaim 30, wherein: said plurality of types of cranial remodeling devicesincludes devices for treatment of specific types of cranial deformities.32. A system in accordance with claim 31, wherein: said specific typesof cranial deformities comprise one or more of plagiocephaly,brachycephaly, and scaphocephaly.
 33. A system in accordance with claim31, wherein: said cranial remodeling device information comprisespredetermined design features that may be incorporated in said cranialremodeling device.
 34. A system in accordance with claim 33, wherein:said predetermined design features comprise standardized structuralconfigurations.
 35. A system in accordance with claim 34, wherein: saidstandardized structural configurations are selected from a groupcomprising one or more of a right anterior corner, a left anteriorcorner, a right posterior corner, a left posterior corner, a fractionalanterior cap, a fractional posterior cap, and a lengthwise strut acrosstop of band.
 36. A system in accordance with claim 34, wherein: saidcranial remodeling device information comprises a selection from a groupcomprising one or more of a right side opening cranial remodeling band,a wide right side opening cranial remodeling band, a left side openingcranial remodeling band, and a wide left side opening cranial remodelingband.
 37. A system in accordance with claim 29, comprising: saidcomputer utilizing said first data to automatically produce a physicalmodel from which said cranial remodeling device is produced.
 38. Asystem for producing cranial remodeling devices to correct for cranialshape abnormalities comprising: a digitizer operable to capture adigital image of a head to produce first digital data; and a computer;one or more neural networks operable on said computer and responsive tosaid first digital data to automatically provide cranial remodelingdevice information for use in fabricating a cranial remodeling devicefor said head.
 39. A system in accordance with claim 38, wherein: saidcranial remodeling device information comprises identification of one ofa plurality of types of cranial remodeling devices.
 40. A system inaccordance with claim 39, wherein: said plurality of types of cranialremodeling devices includes devices for treatment of specificpredetermined types of cranial deformities. 41 A system in accordancewith claim 40, wherein: said specific types of cranial deformitiescomprise one or more of plagiocephaly, brachycephaly, and scaphocephaly.42. A system in accordance with claim 40, wherein: said cranialremodeling device information comprises predetermined design featuresthat may be incorporated in said cranial remodeling device.
 43. A systemin accordance with claim 42, wherein: said predetermined design featurescomprise structural configurations.
 44. A system in accordance withclaim 43, wherein: said structural configurations are selected from agroup comprising one or more of a right anterior corner, a left anteriorcorner, a right posterior corner, a left posterior corner, a fractionalanterior cap, a fractional posterior cap, and a lengthwise strut acrosstop of band.
 45. A system in accordance with claim 38, wherein: saidcranial remodeling device information includes selection of a cranialremodeling device style selected from a group comprising a right sideopening cranial remodeling band, a wide right side opening cranialremodeling band, a left side opening cranial remodeling band, and a wideleft side opening cranial remodeling band.
 46. A system in accordancewith claim 38 comprising: said system comprises apparatus utilizing saidfirst data to automatically produce a physical model from which saidcranial remodeling device is fabricated.
 47. A system for producingcranial remodeling devices to correct for cranial shape abnormalitiescomprising: a digitizer operable to capture a three dimensional digitalimage of a deformed head to produce first digital data; a computeroperable to automatically process said first digital data to producesecond data corresponding to a desired shape for use in forming acranial remodeling device; and said computer automatically providingcranial remodeling device information for use in fabricating a cranialremodeling device for said deformed head.
 48. A system in accordancewith claim 47, wherein: said configuration information comprisesidentification of one of a plurality of styles of cranial remodelingdevices.
 49. A system in accordance with claim 48, wherein: saidplurality of styles of cranial remodeling devices includes devices fortreatment of specific types of cranial deformities.
 50. A system inaccordance with claim 49, wherein: said specific types of cranialdeformities comprise one or more of plagiocephaly, brachycephaly, andscaphocephaly.
 51. A system in accordance with claim 49, wherein: saidcranial remodeling device information comprises predetermined designfeatures that may be incorporated in said cranial remodeling device. 52.A system in accordance with claim 51, wherein: said predetermined designfeatures comprise structural configurations.
 53. A system in accordancewith claim 52, wherein: said structural configurations are selected froma group comprising one or more of a right anterior corner, a leftanterior corner, a right posterior corner, a left posterior corner, afractional anterior cap, a fractional posterior cap, and a lengthwisestrut across top of band.
 54. A system in accordance with claim 47,wherein: said cranial remodeling device information comprises a devicetype selection selected form a group comprising a right side openingcranial remodeling band, a wide right side opening cranial remodelingband, a left side opening cranial remodeling band, and a wide left sideopening cranial remodeling band.
 55. A system in accordance with claim47, wherein: said computer is operable to utilize said second data toautomatically produce a physical model of said desired shape.